几个git链接：

• https://github.com/HFTrader/DeepLearningBook
• https://github.com/ExtremeMart/DeepLearningBook-ReadingNotes
• https://github.com/ExtremeMart/DeepLearningBook-CN

几个问题：

• 1.什么是熵，熵代表什么意义
• 2.熵与KL散度有什么关系
• 3.KL散度代表什么意义，为什么是不对称的
• 4.有向概率图模型和无向图模型的区别
• 5.概率密度函数的数值意义是什么？
• 6.We can thus think of the normal distribution as being the one that inserts the least amount of prior knowledge into a model. 原文中这句话怎么理解

目录：

• 3.1 Why Probability?
• 3.2 Random Variables
• 3.3 Probability Distributions
  • Discrete Variables and Probability Mass Functions
  • Continuous Variables and Probability Density Functions
• 3.4 Marginal Probability
• 3.5 Conditional Probability
• 3.6 The Chain Rule of Conditional Probabilities
• 3.7 Independence and Conditional Independence
• 3.8 Expectation, Variance and Covariance
• 3.9 Common Probability Distributions
  • Bernoulli Distribution
  • Multinoulli Distribution
  • Gaussian Distribution
  • Exponential and Laplace Distributions
  • The Dirac Distribution and Empirical Distribution
  • Mixtures of Distributions
• 3.10 Useful Properties of Common Functions
• 3.11 Bayes’ Rule
• 3.12 Technical Details of Continuous Variables
• 3.13 Information Theory
• 3.14 Structured Probabilistic Models